Enhanced Predictive Value of Lipid Accumulation Product for Identifying Metabolic Syndrome in the General Population of China.


Journal

Nutrients
ISSN: 2072-6643
Titre abrégé: Nutrients
Pays: Switzerland
ID NLM: 101521595

Informations de publication

Date de publication:
17 Jul 2023
Historique:
received: 25 06 2023
revised: 15 07 2023
accepted: 16 07 2023
medline: 31 7 2023
pubmed: 29 7 2023
entrez: 29 7 2023
Statut: epublish

Résumé

The purpose of this research was to evaluate the lipid accumulation product (LAP)'s accuracy and predictive value for identifying metabolic syndrome (MS) in the general Chinese population compared with other obesity indicators. Baseline survey information from a population-based cohort study carried out in Shanghai's Songjiang District was used in this research. Odds ratios (OR) and a 95% confidence interval (CI) were obtained by logistic regression. The ability of each variable to detect MS was assessed using the receiver operating characteristic curve (ROC). The optimum cut-off point for each indicator was selected using Youden's index. The survey involved 35,446 participants in total. In both genders, the prevalence of MS rose as the LAP increased (

Identifiants

pubmed: 37513586
pii: nu15143168
doi: 10.3390/nu15143168
pmc: PMC10383986
pii:
doi:

Types de publication

Journal Article

Langues

eng

Sous-ensembles de citation

IM

Subventions

Organisme : National Key Research and Development Program of China, Precision Medicine Project
ID : 2017YFC0907000

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Auteurs

Qi Shao (Q)

Key Laboratory of Public Health Safety of Ministry of Education, Department of Epidemiology, School of Public Health, Fudan University, Shanghai 200032, China.

Jing Li (J)

Zhongshan Community Health Center, Shanghai 201613, China.

Yiling Wu (Y)

Songjiang District Center for Disease Control and Prevention, Shanghai 201600, China.

Xing Liu (X)

Key Laboratory of Public Health Safety of Ministry of Education, Department of Epidemiology, School of Public Health, Fudan University, Shanghai 200032, China.

Na Wang (N)

Key Laboratory of Public Health Safety of Ministry of Education, Department of Epidemiology, School of Public Health, Fudan University, Shanghai 200032, China.

Yonggen Jiang (Y)

Songjiang District Center for Disease Control and Prevention, Shanghai 201600, China.

Qi Zhao (Q)

NHC Key Laboratory of Health Technology Assessment, Department of Social Medicine, School of Public Health, Fudan University, Shanghai 200032, China.

Genming Zhao (G)

Key Laboratory of Public Health Safety of Ministry of Education, Department of Epidemiology, School of Public Health, Fudan University, Shanghai 200032, China.

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